In this study, we examine how the number of vaccines adminstered in Brazil has changed as a result of the COVID19 pandemic.



Our data comes from DATASUS which tabulates the number of vaccines distributed by state for each month. Here we utilize data from 2015 thorugh 2020.



Total vacccines adminstered across Brazil

Total vacccines adminstered across Brazil









Here we use an I chart followed by a moving range (MR) chart





Below we use an xbar chart in order to incorperate vaccine rates per 1000 people by state.




columns<-rownames(cbind(sort(colSums(All_data[,-c(1,2,3)]), T)[1:5]))

lessthanoneyearsubselected<-as.data.frame(All_data[,c("Date", "pop" ,columns)])

lessthanoneyearsubselected2 <- reshape::melt(lessthanoneyearsubselected, id.vars=c("Date", "pop"))

lessthanoneyearsubselected2$value<-lessthanoneyearsubselected2$value/(lessthanoneyearsubselected2$pop/1000)

ggplot(lessthanoneyearsubselected2, aes(x=Date, y=value, group=variable,color=variable)) +
  geom_line( color="steelblue") + 
  geom_point() +
  xlab("Month")  + ylab("number of vaccinations (per 1000 people)")+
  ggtitle("Vaccines by type (per 1000 persons") +
  theme_clean()+ theme(legend.title = element_blank(),legend.position="bottom",legend.background = element_rect(fill="white", 
                                  size=0.5, linetype="blank"))+
  theme(axis.text.x=element_text(angle=60, hjust=1)) +
  scale_x_date(labels = date_format("%b-%Y"), date_breaks = "3 month",limit=c(as.Date("2015-01-01"),as.Date("2020-10-1")))
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We can examine totals and individuals by age group

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